Edge Computing Integration in IoT Networks for Real-Time Data Processing

This study focuses on the enhancement of loT networks with edge computing to process data in real-time as a solution to replace the current cloud models that would take time to manage large amounts of loT data. Edge computing allows data to be analyzed and processed locally and that leads to a marked decrease in latency, better usage of bandwidth resources and the improvement of the overall efficiency of applications that require timely analysis. The approach entails logging authentic data from the smart city context at real time, analyzing them at the edge nodes with the support of the proposed machine learning models, and assessing the results against the chosen performance indicators like the latency, bandwidth utilization and consumption of the energy, as well as the accuracy of the models. The findings revealed that edge computing offers a much higher efficiency in the processing of the data while at the same time retaining a very high level of accuracy necessary for real-time loT applications. The study also covers the issues associated with the integration of edge computing into loT networks, including security issues and the question of resource distribution and utilization and prospects for edge AI and renewable energy to increase the scalability and sustainability of edge-based solutions in loT networks.